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In order to adapt map tiles to high resolution display devices, some internet map providers pre-generate and store more than two sizes of map tiles. However, it is inefficient to download larger hi...

In order to explore the information management, the cloud computing technology is applied to the field of geographic information system, and remote sensing data storage and management system based on Hadoop is studied and realized. The main function of this system includes that the remote sensing data storage module provides the remote sensing data download function for data administrator, supports HTTP protocol and FTP protocol multi-threaded distributed HTTP download. The parallel constructing algorithm of remote sensing image of Pyramid based on Map Reduce is realized by the module, and layered cutting and block storage of massive remote sensing data are carried out. The GDAL open source library suitable for fast read raster data is used and it provides data resource for remote sensing data parallel cutting. In addition, the Geo Web Cache open source tile map service middleware is adopted and HBase is introduced as the storage support of tiles, which can deal with a large number of users’ visit, including loading and drag of maps. The system test is carried out to verify the effectiveness and practicability of the method proposed. The test results can show that the remote sensing data storage and management system based on Hadoop can effectively handle remote sensing data and improve the user’s experience. It is concluded that the information management system has high effectiveness and good practicability.

This paper describes the process of digitalization of old globes that brings with it the possibility to use globes in their digital form. Created digital models are available to the general public through modern technology in the Internet network. This gives an opportunity to study old globes located in various historical collections, and prevent damage of the originals. Another benefit of digitization is also a possibility of comparing different models both among themselves and with current map data by increasing the transparency of individual layers. Digitization is carried out using special device that allows digitizing globes with a diameter ranging from 5 cm to 120 cm. This device can be easily disassembled, and it is fully mobile therefore the globes can be digitized in the place of its storage. Image data of globe surface are acquired by digital camera firmly fastened to the device. Acquired image data are then georeferenced by using a method of complex adjustment. The last step of digitization is publication of the final models that is realized by two ways. The first option is in the form of 3D model through JavaScript library Cesium or Google Earth plug-in in the Web browser. The second option is as a georeferenced map using Tile Map Service.

Planning the use of marine areas requires support in allocating space to particular activities, assessing impacts and cumulative effects of activities, and generic decision making. The SmartSea project studies the Gulf of Bothnia, the northernmost arm of the Baltic Sea, as resource for sustainable growth. One objective of the project is to provide an open source and open access toolbox for marine spatial planning. Here we present a vision for the toolbox and an initial prototype. The vision is based on a model of information system as a meeting point of users, information providers, and tasks. A central technical requirement for the system was found to be a data model and related database of spatial planning and a programmable map service. An initial prototype of the system exists comprising a database, data browser/editor, dynamic tile map service, web mapping application, and extensions for a desktop GIS.

Within a digital map service environment, the rapid growth of Spatial Big-Data is driving new requirements for effective mechanisms for massive online vector map tile processing. The emergence of Not Only SQL (NoSQL) databases has resulted in a new data storage and management model for scalable spatial data deployments and fast tracking. They better suit the scenario of high-volume, low-latency network map services than traditional standalone high-performance computer (HPC) or relational databases. In this paper, we propose a flexible storage framework that provides feasible methods for tiled map data parallel clipping and retrieval operations within a distributed NoSQL database environment. We illustrate the parallel vector tile generation and querying algorithms with the MapReduce programming model. Three different processing approaches, including local caching, distributed file storage, and the NoSQL-based method, are compared by analyzing the concurrent load and calculation time. An online geological vector tile map service prototype was developed to embed our processing framework in the China Geological Survey Information Grid. Experimental results show that our NoSQL-based parallel tile management framework can support applications that process huge volumes of vector tile data and improve performance of the tiled map service.